Add flake8 comma rule

dspy/adapters/base.py

@@ -43,7 +43,7 @@ def _call_preprocess(
                 raise ValueError(
                     f"You provided an output field {tool_call_output_field_name} to receive the tool calls information, "
                     "but did not provide any tools as the input. Please provide a list of tools as the input by adding an "
-                    "input field with type `list[dspy.Tool]`."
+                    "input field with type `list[dspy.Tool]`.",
                 )
 
             if tool_call_output_field_name and litellm.supports_function_calling(model=lm.model):
@@ -58,7 +58,7 @@ def _call_preprocess(
 
                 signature_for_native_function_calling = signature.delete(tool_call_output_field_name)
                 signature_for_native_function_calling = signature_for_native_function_calling.delete(
-                    tool_call_input_field_name
+                    tool_call_input_field_name,
                 )
 
                 return signature_for_native_function_calling
@@ -344,15 +344,15 @@ def format_demos(self, signature: type[Signature], demos: list[dict[str, Any]])
                 {
                     "role": "user",
                     "content": self.format_user_message_content(signature, demo, prefix=incomplete_demo_prefix),
-                }
+                },
             )
             messages.append(
                 {
                     "role": "assistant",
                     "content": self.format_assistant_message_content(
-                        signature, demo, missing_field_message="Not supplied for this particular example. "
+                        signature, demo, missing_field_message="Not supplied for this particular example. ",
                     ),
-                }
+                },
             )
 
         for demo in complete_demos:
@@ -361,9 +361,9 @@ def format_demos(self, signature: type[Signature], demos: list[dict[str, Any]])
                 {
                     "role": "assistant",
                     "content": self.format_assistant_message_content(
-                        signature, demo, missing_field_message="Not supplied for this conversation history message. "
+                        signature, demo, missing_field_message="Not supplied for this conversation history message. ",
                     ),
-                }
+                },
             )
 
         return messages
@@ -419,13 +419,13 @@ def format_conversation_history(
                 {
                     "role": "user",
                     "content": self.format_user_message_content(signature, message),
-                }
+                },
             )
             messages.append(
                 {
                     "role": "assistant",
                     "content": self.format_assistant_message_content(signature, message),
-                }
+                },
             )
 
         # Remove the history field from the inputs

dspy/adapters/chat_adapter.py

@@ -238,10 +238,10 @@ def format_finetune_data(
         wrapped in a dictionary with a "messages" key.
         """
         system_user_messages = self.format(  # returns a list of dicts with the keys "role" and "content"
-            signature=signature, demos=demos, inputs=inputs
+            signature=signature, demos=demos, inputs=inputs,
         )
         assistant_message_content = self.format_assistant_message_content(  # returns a string, without the role
-            signature=signature, outputs=outputs
+            signature=signature, outputs=outputs,
         )
         assistant_message = {"role": "assistant", "content": assistant_message_content}
         messages = system_user_messages + [assistant_message]

dspy/adapters/json_adapter.py

@@ -72,7 +72,7 @@ def __call__(
 
         try:
             structured_output_model = _get_structured_outputs_response_format(
-                signature, self.use_native_function_calling
+                signature, self.use_native_function_calling,
             )
             lm_kwargs["response_format"] = structured_output_model
             return super().__call__(lm, lm_kwargs, signature, demos, inputs)
@@ -201,7 +201,7 @@ def format_field_with_value(self, fields_with_values: dict[FieldInfoWithName, An
             return json.dumps(serialize_for_json(d), indent=2)
 
     def format_finetune_data(
-        self, signature: type[Signature], demos: list[dict[str, Any]], inputs: dict[str, Any], outputs: dict[str, Any]
+        self, signature: type[Signature], demos: list[dict[str, Any]], inputs: dict[str, Any], outputs: dict[str, Any],
     ) -> dict[str, list[Any]]:
         # TODO: implement format_finetune_data method in JSONAdapter
         raise NotImplementedError
@@ -225,7 +225,7 @@ def _get_structured_outputs_response_format(
         annotation = field.annotation
         if get_origin(annotation) is dict:
             raise ValueError(
-                f"Field '{name}' has an open-ended mapping type which is not supported by Structured Outputs."
+                f"Field '{name}' has an open-ended mapping type which is not supported by Structured Outputs.",
             )
 
     fields = {}

dspy/adapters/two_step_adapter.py

@@ -46,7 +46,7 @@ def __init__(self, extraction_model: LM, **kwargs):
         self.extraction_model = extraction_model
 
     def format(
-        self, signature: type[Signature], demos: list[dict[str, Any]], inputs: dict[str, Any]
+        self, signature: type[Signature], demos: list[dict[str, Any]], inputs: dict[str, Any],
     ) -> list[dict[str, Any]]:
         """
         Format a prompt for the first stage with the main LM.

dspy/adapters/types/audio.py

@@ -35,8 +35,8 @@ def format(self) -> list[dict[str, Any]]:
             "type": "input_audio",
             "input_audio": {
                 "data": data,
-                "format": self.audio_format
-            }
+                "format": self.audio_format,
+            },
         }]
 
 
@@ -85,7 +85,7 @@ def from_file(cls, file_path: str) -> "Audio":
 
     @classmethod
     def from_array(
-        cls, array: Any, sampling_rate: int, format: str = "wav"
+        cls, array: Any, sampling_rate: int, format: str = "wav",
     ) -> "Audio":
         """
         Process numpy-like array and encode it as base64. Uses sampling rate and audio format for encoding.

dspy/adapters/types/tool.py

@@ -179,7 +179,7 @@ def __call__(self, **kwargs):
             else:
                 raise ValueError(
                     "You are calling `__call__` on an async tool, please use `acall` instead or set "
-                    "`allow_async=True` to run the async tool in sync mode."
+                    "`allow_async=True` to run the async tool in sync mode.",
                 )
         return result
 
@@ -306,7 +306,7 @@ def format(self) -> list[dict[str, Any]]:
                     }
                     for tool_call in self.tool_calls
                 ],
-            }
+            },
         ]
 
 

dspy/clients/__init__.py

@@ -47,7 +47,7 @@ def configure_cache(
     if enable_disk_cache and enable_litellm_cache:
         raise ValueError(
             "Cannot enable both LiteLLM and DSPy on-disk cache, please set at most one of `enable_disk_cache` or "
-            "`enable_litellm_cache` to True."
+            "`enable_litellm_cache` to True.",
         )
 
     if enable_litellm_cache:

dspy/clients/databricks.py

@@ -32,7 +32,7 @@ def status(self):
         except ImportError:
             raise ImportError(
                 "To use Databricks finetuning, please install the databricks_genai package via "
-                "`pip install databricks_genai`."
+                "`pip install databricks_genai`.",
             )
         run = fm.get(self.finetuning_run)
         return run.status
@@ -84,7 +84,7 @@ def deploy_finetuned_model(
         model_name = model.replace(".", "_")
 
         get_endpoint_response = requests.get(
-            url=f"{databricks_host}/api/2.0/serving-endpoints/{model_name}", json={"name": model_name}, headers=headers
+            url=f"{databricks_host}/api/2.0/serving-endpoints/{model_name}", json={"name": model_name}, headers=headers,
         )
 
         if get_endpoint_response.status_code == 200:
@@ -98,8 +98,8 @@ def deploy_finetuned_model(
                         "entity_version": model_version,
                         "min_provisioned_throughput": min_provisioned_throughput,
                         "max_provisioned_throughput": max_provisioned_throughput,
-                    }
-                ]
+                    },
+                ],
             }
 
             response = requests.put(
@@ -120,23 +120,23 @@ def deploy_finetuned_model(
                             "entity_version": model_version,
                             "min_provisioned_throughput": min_provisioned_throughput,
                             "max_provisioned_throughput": max_provisioned_throughput,
-                        }
-                    ]
+                        },
+                    ],
                 },
             }
 
             response = requests.post(url=f"{databricks_host}/api/2.0/serving-endpoints", json=data, headers=headers)
 
         if response.status_code == 200:
             logger.info(
-                f"Successfully started creating/updating serving endpoint {model_name} on Databricks model serving!"
+                f"Successfully started creating/updating serving endpoint {model_name} on Databricks model serving!",
             )
         else:
             raise ValueError(f"Failed to create serving endpoint: {response.json()}.")
 
         logger.info(
             f"Waiting for serving endpoint {model_name} to be ready, this might take a few minutes... You can check "
-            f"the status of the endpoint at {databricks_host}/ml/endpoints/{model_name}"
+            f"the status of the endpoint at {databricks_host}/ml/endpoints/{model_name}",
         )
         from openai import OpenAI
 
@@ -150,7 +150,7 @@ def deploy_finetuned_model(
             try:
                 if data_format == TrainDataFormat.CHAT:
                     client.chat.completions.create(
-                        messages=[{"role": "user", "content": "hi"}], model=model_name, max_tokens=1
+                        messages=[{"role": "user", "content": "hi"}], model=model_name, max_tokens=1,
                     )
                 elif data_format == TrainDataFormat.COMPLETION:
                     client.completions.create(prompt="hi", model=model_name, max_tokens=1)
@@ -161,7 +161,7 @@ def deploy_finetuned_model(
 
         raise ValueError(
             f"Failed to create serving endpoint {model_name} on Databricks model serving platform within "
-            f"{deploy_timeout} seconds."
+            f"{deploy_timeout} seconds.",
         )
 
     @staticmethod
@@ -179,22 +179,22 @@ def finetune(
                 train_data_format = TrainDataFormat.COMPLETION
             else:
                 raise ValueError(
-                    f"String `train_data_format` must be one of 'chat' or 'completion', but received: {train_data_format}."
+                    f"String `train_data_format` must be one of 'chat' or 'completion', but received: {train_data_format}.",
                 )
 
         if "train_data_path" not in train_kwargs:
             raise ValueError("The `train_data_path` must be provided to finetune on Databricks.")
         # Add the file name to the directory path.
         train_kwargs["train_data_path"] = DatabricksProvider.upload_data(
-            train_data, train_kwargs["train_data_path"], train_data_format
+            train_data, train_kwargs["train_data_path"], train_data_format,
         )
 
         try:
             from databricks.model_training import foundation_model as fm
         except ImportError:
             raise ImportError(
                 "To use Databricks finetuning, please install the databricks_genai package via "
-                "`pip install databricks_genai`."
+                "`pip install databricks_genai`.",
             )
 
         if "register_to" not in train_kwargs:
@@ -224,7 +224,7 @@ def finetune(
             elif job.run.status.display_name == "Failed":
                 raise ValueError(
                     f"Finetuning run failed with status: {job.run.status.display_name}. Please check the Databricks "
-                    f"workspace for more details. Finetuning job's metadata: {job.run}."
+                    f"workspace for more details. Finetuning job's metadata: {job.run}.",
                 )
             else:
                 time.sleep(60)
@@ -236,7 +236,7 @@ def finetune(
         model_to_deploy = train_kwargs.get("register_to")
         job.endpoint_name = model_to_deploy.replace(".", "_")
         DatabricksProvider.deploy_finetuned_model(
-            model_to_deploy, train_data_format, databricks_host, databricks_token, deploy_timeout
+            model_to_deploy, train_data_format, databricks_host, databricks_token, deploy_timeout,
         )
         job.launch_completed = True
         # The finetuned model name should be in the format: "databricks/<endpoint_name>".
@@ -266,7 +266,7 @@ def _get_workspace_client() -> "WorkspaceClient":
     except ImportError:
         raise ImportError(
             "To use Databricks finetuning, please install the databricks-sdk package via "
-            "`pip install databricks-sdk`."
+            "`pip install databricks-sdk`.",
         )
     return WorkspaceClient()
 
@@ -277,7 +277,7 @@ def _create_directory_in_databricks_unity_catalog(w: "WorkspaceClient", databric
     if not match:
         raise ValueError(
             f"Databricks Unity Catalog path must be in the format '/Volumes/<catalog>/<schema>/<volume>/...', but "
-            f"received: {databricks_unity_catalog_path}."
+            f"received: {databricks_unity_catalog_path}.",
         )
 
     catalog = match.group("catalog")
@@ -290,7 +290,7 @@ def _create_directory_in_databricks_unity_catalog(w: "WorkspaceClient", databric
     except Exception:
         raise ValueError(
             f"Databricks Unity Catalog volume does not exist: {volume_path}, please create it on the Databricks "
-            "workspace."
+            "workspace.",
         )
 
     try:
@@ -326,32 +326,32 @@ def _validate_chat_data(data: dict[str, Any]):
     if "messages" not in data:
         raise ValueError(
             "Each finetuning data must be a dict with a 'messages' key when `task=CHAT_COMPLETION`, but "
-            f"received: {data}"
+            f"received: {data}",
         )
 
     if not isinstance(data["messages"], list):
         raise ValueError(
             "The value of the 'messages' key in each finetuning data must be a list of dicts with keys 'role' and "
-            f"'content' when `task=CHAT_COMPLETION`, but received: {data['messages']}"
+            f"'content' when `task=CHAT_COMPLETION`, but received: {data['messages']}",
         )
 
     for message in data["messages"]:
         if "role" not in message:
             raise ValueError(f"Each message in the 'messages' list must contain a 'role' key, but received: {message}.")
         if "content" not in message:
             raise ValueError(
-                f"Each message in the 'messages' list must contain a 'content' key, but received: {message}."
+                f"Each message in the 'messages' list must contain a 'content' key, but received: {message}.",
             )
 
 
 def _validate_completion_data(data: dict[str, Any]):
     if "prompt" not in data:
         raise ValueError(
             "Each finetuning data must be a dict with a 'prompt' key when `task=INSTRUCTION_FINETUNE`, but "
-            f"received: {data}"
+            f"received: {data}",
         )
     if "response" not in data and "completion" not in data:
         raise ValueError(
             "Each finetuning data must be a dict with a 'response' or 'completion' key when "
-            f"`task=INSTRUCTION_FINETUNE`, but received: {data}"
+            f"`task=INSTRUCTION_FINETUNE`, but received: {data}",
         )

dspy/clients/lm.py

@@ -136,7 +136,7 @@ def forward(self, prompt=None, messages=None, **kwargs):
                 "You can inspect the latest LM interactions with `dspy.inspect_history()`. "
                 "To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. "
                 f"You may also consider increasing the temperature (currently {self.kwargs['temperature']}) "
-                " if the reason for truncation is repetition."
+                " if the reason for truncation is repetition.",
             )
 
         if not getattr(results, "cache_hit", False) and dspy.settings.usage_tracker and hasattr(results, "usage"):
@@ -166,7 +166,7 @@ async def aforward(self, prompt=None, messages=None, **kwargs):
                 "You can inspect the latest LM interactions with `dspy.inspect_history()`. "
                 "To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. "
                 f"You may also consider increasing the temperature (currently {self.kwargs['temperature']}) "
-                " if the reason for truncation is repetition."
+                " if the reason for truncation is repetition.",
             )
 
         if not getattr(results, "cache_hit", False) and dspy.settings.usage_tracker and hasattr(results, "usage"):

dspy/clients/lm_local.py

@@ -32,7 +32,7 @@ def launch(lm: "LM", launch_kwargs: dict[str, Any] | None = None):
         except ImportError:
             raise ImportError(
                 "For local model launching, please install sglang."
-                "Navigate to https://docs.sglang.ai/start/install.html for the latest installation instructions!"
+                "Navigate to https://docs.sglang.ai/start/install.html for the latest installation instructions!",
             )
 
         if hasattr(lm, "process"):
@@ -195,7 +195,7 @@ def train_sft_locally(model_name, train_data, train_kwargs):
     except ImportError:
         raise ImportError(
             "For local finetuning, please install torch, transformers, and trl "
-            "by running `pip install -U torch transformers accelerate trl peft`"
+            "by running `pip install -U torch transformers accelerate trl peft`",
         )
 
     device = train_kwargs.get("device", None)
@@ -220,7 +220,7 @@ def train_sft_locally(model_name, train_data, train_kwargs):
     if "max_seq_length" not in train_kwargs:
         train_kwargs["max_seq_length"] = 4096
         logger.info(
-            f"The 'train_kwargs' parameter didn't include a 'max_seq_length', defaulting to {train_kwargs['max_seq_length']}"
+            f"The 'train_kwargs' parameter didn't include a 'max_seq_length', defaulting to {train_kwargs['max_seq_length']}",
         )
 
     from datasets import Dataset